Posthuman performativity, gender and ‘school bullying’ : exploring the material-discursive intra-actions of skirts, hair, sluts, and poofs
Bibliographic record
Abstract
In this article we take off from critiques of psychological and school bullying typologies as creating problematic binary categories of bully and victim and neglecting socio-cultural aspects of gender and sexuality. We review bullying research informed by Judith Butler’s theories of discursive performativity, which help us to understand how subjectification works through performative repetitions of heterosexual gender norms. We then build on these insights drawing on the feminist new materialist approach of Karen Barad’s posthuman performativity, which we argue enlarges our scope of inquiry in profound ways. Barad’s theories suggest we move from psychological models of the inter- personal, and from Butlerian notions of discursive subjectification, to ideas of discursive-material intra-action to consider the more-than-human relationalities of bullying. Throughout the article, we demonstrate the approach using examples from qualitative research with teens in the UK and Australia, exploring non-human agentic matter such as space, objects and time as shaping the constitution of gender and sexual bullying events. Specifically we examine the discursive-material agential intra-actions of skirts and hair through which ‘girl’ and ‘boy’ and ‘slut’ and ‘gay’ materialise in school spacetimematterings. In our conclusion we briefly suggest how the new materialism helps to shift the frame of attention and responses informing gendered intra-actions in schools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".